{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic data set generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package loaded\n",
      "Current folder is /home/enginius/github/tensorflow-101/notebooks\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from scipy.misc import imread, imresize\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "print (\"Package loaded\") \n",
    "cwd = os.getcwd()\n",
    "print (\"Current folder is %s\" % (cwd) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPECIFY THE FOLDER PATHS \n",
    "## + RESHAPE SIZE + GRAYSCALE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your images should be at\n",
      " [0/4] /home/enginius/github/tensorflow-101/notebooks/../../img_dataset/celebs/George_W_Bush\n",
      " [1/4] /home/enginius/github/tensorflow-101/notebooks/../../img_dataset/celebs/Arnold_Schwarzenegger\n",
      " [2/4] /home/enginius/github/tensorflow-101/notebooks/../../img_dataset/celebs/Junichiro_Koizumi\n",
      " [3/4] /home/enginius/github/tensorflow-101/notebooks/../../img_dataset/celebs/Vladimir_Putin\n",
      "Data will be saved to /home/enginius/github/tensorflow-101/notebooks/data/custom_data.npz\n"
     ]
    }
   ],
   "source": [
    "# Training set folder \n",
    "paths = {\"../../img_dataset/celebs/Arnold_Schwarzenegger\"\n",
    "        , \"../../img_dataset/celebs/Junichiro_Koizumi\"\n",
    "        , \"../../img_dataset/celebs/Vladimir_Putin\"\n",
    "        , \"../../img_dataset/celebs/George_W_Bush\"}\n",
    "# The reshape size\n",
    "imgsize = [64, 64]\n",
    "# Grayscale\n",
    "use_gray = 1\n",
    "# Save name\n",
    "data_name = \"custom_data\"\n",
    "\n",
    "print (\"Your images should be at\")\n",
    "for i, path in enumerate(paths):\n",
    "    print (\" [%d/%d] %s/%s\" % (i, len(paths), cwd, path)) \n",
    "\n",
    "print (\"Data will be saved to %s\" \n",
    "       % (cwd + '/data/' + data_name + '.npz'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RGB 2 GRAY FUNCTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rgb2gray(rgb):\n",
    "    if len(rgb.shape) is 3:\n",
    "        return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
    "    else:\n",
    "        # print (\"Current Image if GRAY!\")\n",
    "        return rgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAD IMAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total 681 images loaded.\n"
     ]
    }
   ],
   "source": [
    "nclass     = len(paths)\n",
    "valid_exts = [\".jpg\",\".gif\",\".png\",\".tga\", \".jpeg\"]\n",
    "imgcnt     = 0\n",
    "for i, relpath in zip(range(nclass), paths):\n",
    "    path = cwd + \"/\" + relpath\n",
    "    flist = os.listdir(path)\n",
    "    for f in flist:\n",
    "        if os.path.splitext(f)[1].lower() not in valid_exts:\n",
    "            continue\n",
    "        fullpath = os.path.join(path, f)\n",
    "        currimg  = imread(fullpath)\n",
    "        # Convert to grayscale  \n",
    "        if use_gray:\n",
    "            grayimg  = rgb2gray(currimg)\n",
    "        else:\n",
    "            grayimg  = currimg\n",
    "        # Reshape\n",
    "        graysmall = imresize(grayimg, [imgsize[0], imgsize[1]])/255.\n",
    "        grayvec   = np.reshape(graysmall, (1, -1))\n",
    "        # Save \n",
    "        curr_label = np.eye(nclass, nclass)[i:i+1, :]\n",
    "        if imgcnt is 0:\n",
    "            totalimg   = grayvec\n",
    "            totallabel = curr_label\n",
    "        else:\n",
    "            totalimg   = np.concatenate((totalimg, grayvec), axis=0)\n",
    "            totallabel = np.concatenate((totallabel, curr_label), axis=0)\n",
    "        imgcnt    = imgcnt + 1\n",
    "print (\"Total %d images loaded.\" % (imgcnt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DIVIDE TOTAL DATA INTO TRAINING AND TEST SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of 'trainimg' is (408, 4096)\n",
      "Shape of 'trainlabel' is (408, 4)\n",
      "Shape of 'testimg' is (273, 4096)\n",
      "Shape of 'testlabel' is (273, 4)\n"
     ]
    }
   ],
   "source": [
    "def print_shape(string, x):\n",
    "    print (\"Shape of '%s' is %s\" % (string, x.shape,))\n",
    "    \n",
    "randidx    = np.random.randint(imgcnt, size=imgcnt)\n",
    "trainidx   = randidx[0:int(3*imgcnt/5)]\n",
    "testidx    = randidx[int(3*imgcnt/5):imgcnt]\n",
    "trainimg   = totalimg[trainidx, :]\n",
    "trainlabel = totallabel[trainidx, :]\n",
    "testimg    = totalimg[testidx, :]\n",
    "testlabel  = totallabel[testidx, :]\n",
    "print_shape(\"trainimg\", trainimg)\n",
    "print_shape(\"trainlabel\", trainlabel)\n",
    "print_shape(\"testimg\", testimg)\n",
    "print_shape(\"testlabel\", testlabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAVE TO NPZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved to /home/enginius/github/tensorflow-101/notebooks/data/custom_data.npz\n"
     ]
    }
   ],
   "source": [
    "savepath = cwd + \"/data/\" + data_name + \".npz\"\n",
    "np.savez(savepath, trainimg=trainimg, trainlabel=trainlabel\n",
    "         , testimg=testimg, testlabel=testlabel, imgsize=imgsize, use_gray=use_gray)\n",
    "print (\"Saved to %s\" % (savepath))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAD TO CHECK!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "408 train images loaded\n",
      "273 test images loaded\n",
      "Loaded from to /home/enginius/github/tensorflow-101/notebooks/data/custom_data.npz\n"
     ]
    }
   ],
   "source": [
    "# Load them!\n",
    "cwd = os.getcwd()\n",
    "loadpath = cwd + \"/data/\" + data_name + \".npz\"\n",
    "l = np.load(loadpath)\n",
    "\n",
    "# See what's in here\n",
    "l.files\n",
    "\n",
    "# Parse data\n",
    "trainimg_loaded = l['trainimg']\n",
    "trainlabel_loaded = l['trainlabel']\n",
    "testimg_loaded = l['testimg']\n",
    "testlabel_loaded = l['testlabel']\n",
    "\n",
    "print (\"%d train images loaded\" % (trainimg_loaded.shape[0]))\n",
    "print (\"%d test images loaded\" % (testimg_loaded.shape[0]))\n",
    "print (\"Loaded from to %s\" % (savepath))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOT RANDOMLY SELECTED TRAIN IMAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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VUTDbpWi0byUxgo7eagizveB6jq1JAyxfvjwvL1q0KC/rCG8aQCoqq46itoato6n2U/tz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\nueMMOC7kjjPguJA7zoDjQu4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5ae3066d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VcUbbbfnpNGj27NlFWqcH2j5Le1ptnjac0fVu91PkMABJ5otIwhEPN/7/LvDf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\ngUAgEAgEAoFAIBAIBAKBQCAQCAwW/x8ql/ZeW/AuuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d4145550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5afb07fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ria/FWS40wxBG20j+OpxqfiUwETeafxnLhWYYUUbbLrRbshe4fOQfAN4DfILE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\n2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d45528d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S5qKqpFJKaNRdqX9sGQAlSpyK+aXXxvatlZYrzde9+BNPLB1TNkWYmpZqn7UM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\n04BXCC+EK4FFqQKbLuRGn5S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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5ae5606d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XNfIzKYHlngYWAd8IvJeGedCgHX/rwIEDPPfcc+zYsaOK8aVSTVELp0o7s3yr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5afb91610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B1Bu/VHZr2RLZp1Ibq+Etdp0qk3VdfvHixXk5ZjgscgGG+FRJv+vU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\nHMdxHMdxHKcX+P93CrLEK7CZ4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5afb07fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+pwJP7NiR/9bMGmFx7yC3DECz047eoxiqzJBlfhcKjTu4aNEiFi1alNXxjW+0\nBkot33Yrpk+fzvTp07P/b7vtttZTysRdP5kwsfYR1vAjSAg4FAv5JIJibWTjcy1wKyEf2g+Bj5Cb\n0BwOB0MSd/1dwB83zt0DvLddhUVC/hAh51IrduB50ByOKGqwkxfFXf+HxqcUOu7xVuSnbkg5qlTR\nrsfaLepXmXOVritlVp9w1aQbhVa6WyabitF0dVhRhxRNlWy7yPSY+o8rdZ82bVpWNvqv1yl9njFj\nRlZW2h3LQqPBIVJ03YJbvOENb8iO6dJMn3tsiVXmOcWcZLQ/aqFIOfPYuKoEN0nhUPN4czgcFdFz\nQm5vzpj9ueq+8KK3e5lyUXux4ynbuM7UsRlAFWgKvRfqAmozo+5T11lIFVNW1j3rqWitOmuffvrp\n/fquM6/Obrp33sYd25sOzUxGd8bNmhUsO2pV0fui4Z9is7o+m1RUWe2zXafj1/a0jSI36IGGEPNd\naA5Hl6PnZnKHo9fQc0LeTlmWuhlFdCcV+bOoPv1e64jZqiFXxmh/VFGmlFCPG3VXd1FVpinlVzvx\nE088ATSHZopFgYWc5mp/VUGYUmjZbjC1dyudVcWa9t9COunyQhWPWocp2yBfgihd135qP/R+GlLP\nN/X8imh+KulE7Dd3EHeh1QqfyR2OmuFC7nB0OXpOyNtpxDupGY+dUzXum52jWubYTjBo1mAbfVRq\nqBpsbVsDOpi2Wu3dsXq1DtV26/fati4JzC03Va/2U6m50WO1jWtUVtWun3rqqVn5qaeeohV635Tm\nxzTmeq6OKZWGOmbNUbqufgcayCKWFnug6Dkhdzh6DW5Cczi6HD03k8eocut3rd9XCa3cScpvbetu\nMqWiSt2VrprGWJ0plJYqDVbt8oQJE4BmTbw6xmggCNPQq8ZdteEpCm475HSXmtahdFb7afdCqa+O\nWevQZ2a75fTcVLCNWAAMbS+lUS9C6jekz8fKKUtLFfSckDscvQYXcopn5CrJFVJ1DPRGx/Ye69td\nZ/WUEs5mIZ29Uv3RGdBs7RYGCppTJq1duzYrm41aUxypEk5t7coW1q1b1++YMYjWcWif7R6ksp7q\nPdKZ2txvtV6tQ2fqWIgsZUBah9632Iwci/Lb2nbRdd0yk3dHygqHYxhhkIEcIYRkXgM8ToiGnML5\nhD3lV7Trj9N1h6NmDFFI5sMIkZlupiBy60G1k6f29lbZhZaKpDrQUEFFS4mUS21sh5Ta19WmrHRW\n7c5mt1UYOPGVAAASaElEQVQbtu7vVgpubagyTilsavljLqUa5kmpe2rnVYzOar26Q06XENYPvU4V\nbHo89vxSWVZTdLzo+5R9PTbuOpZ8A4CGZIY8JHOrkH8UuJ4wm7eF03WHo2YMkq7HQjK3Rj89hSD4\nX7cm2/XH6brDUTMGSdfLXPxVQsaiPgJVr4WuH0aIIrkFuJQKudDahX+qsoMsVZdioJv8FbG6YwEv\nWtuLjUVpsELdM5XG23X6vVJ+peZ2XHd0KRXVelVDbUsCtZ2rfV2XElouos8axVbHbRr/FM1X6DlW\nh2rwqyTPSNH1IopeJrJrEdoJ+VNPPRV19dVTKA7JvJBA4yFEVH4rIdvKz2IVlhXyjxEyNNhi8dN4\nLjSHI4p2Qj558uSmF+Ly5ctbTykTkvl0KX8HuImEgEO5NfmpwNsI2RXt1ee50ByOBAa5JteQzI8S\nGLOFZP6j2AVFKDOTfwX4M+AYOVY6F1qRA0sMdTghFGnly1C/GF1TCqu0NEbzlA7qjid1F33sscey\n8uOPPw40R09VN1rNG2bHVautqZRVgx3bvaXUXh1EVEOv1NZodypoRCpSrI1b+5NyVdV+WFnvt44j\nZdkwpLT2qWisVl8qT10V1LBBpSgks+JDRZUVzeRvJ2RqWEl6cd9HOWWBw9ETqMEZplYUzeQXEqj5\n24AjCLP5tVTIhfbQQw8B4e0+ceLEJuWRwzEcsWLFClasWNERxdvBQJGQ/3njA3ARIUXqBwkKtysp\nkQvt3HPPBXJq29fXF715VfKbVaXdVYJUxKBtKEVXzbZSyVisslRwBKWjtmNL84bpLjTLK6bXad9V\nE33BBXm2W/WFNzquNF+XEqmdc6aB1+QS2k89VxNJ2PFYAApIx1mL7QrTvilFj1k5Ur+LlO+69XPk\nyJHMmTOHOXPmZP28+uqrqYJDTchbYb3/Ap4LzeGI4lAW8jsaH6iQC62sW6sqRIqUX0VttSJm4029\n0Yvq1Zlcbdg6y1o7OqPHlFEAc+bMycozZ84EaMp6qXWoC6zNjDrT33nnnVlZlWLvf//7s7JFeVUG\noQxA95PHorXqvdCorBqtVpVsVoceSym0dKx2jp6bSncUYwBl7N0x5V037kJzjzeHo2a4kDscXY6e\ni/HW7q1W9Y1XJYzTQBVvVXa9qYIpFnVVofZnVVJpwAarW9tLZR+1+iwhQ2sbqphSG7bZ0tWmrtC+\naz+tDlXY6RJLr4vdw1Qk2VTQCCunno1eFwtIUWbnoJa7OWiEz+QOR81wIXc4uhw9K+RV6HNRHUV2\n9FS5Kl1v1weIB/WHnOamqGgqla5p3VPpdbUOO1c3O2gaYNXgW4RWyOlsbKdY6/hUu27j0/Z0/EXu\noNpeKiJq0Vo2FattoM8vFbSkbF1l2hgO8Jnc4agZLuQOR5ej54S8yM0wdqyIfg2Udqe+V3oZ21mm\n52pstIcffjgrq4unOZTs3r07O6babK1PqbIlVdA+aAhopeumSddj6pyjx9X91miuOr1oWe+FOtSY\ny61q13XZUeTMVIZqxzTbKYHROhSx3WRVhC4VYKIKes6E5nD0GobbTO6BHB2OmlHDVtOiuOvvAFYR\ntoCvAN7Urj8H1RkmdV5KA2oUrGretCox5WLnaEyuH/3oR1l54cKFWVnDJa9fvx5o3nmlTi+q+Y5p\n61WrrZpxpf+2PFAHGKXlujxQv3JzRNEYcPq99lnLFqQiFYAhRccNVawnEKe8qT0GRfsilLoX+bQX\nxYArg0HO5GXirt8C/LRRngP8GDiDBHwmdzhqxiBnco27/hp53HXFbimPA56jDQ5qcgVFmbdm7C2d\nKhfZ5WP1QvNb33Z43XjjjdkxC9EEcNlll2VldTm1fGPPPpvH0tAdWxo4Q2dtm6l1HKmdV6ac01kz\npfzS47aTLZWUQvtsjESPv+td78qOpezdKf+BKihSnKaOWzmW5yx1bt0YZL2xuOsXRM67HPhbQtCW\n321XoSveHI6a0U7It2/f3pSaOnZ5yWZ+0vgsJURrOjN1ogu5w1Ez2rGXE044ocksqoE8GygTd11x\nF0GOTwCej53QcSFvp/SK5Q/TayCeH6sKRVekdjHpDqpHHnkkK99+++0APPnkk9kxVZop9Li5l2pq\nY61jypQpWVlt4kb5UyGY9NxY1NFUMAa1d1t9atffsiX/Dem90GXF9ddf3+/7yy/PI3Groq+K0iwF\nO78MRY/R8RRdL6LudVD4QdZRJu76DGADYdY/r3EsKuDgM7nDUTsGKeQad/0w4GryuOsQQjO/E/j3\nBMXcLuC97Sp0IXc4akYNbKAo7vqXGp9SKCvkm4CdwH7C22MxJfOhxVwDjVZWoeipuvSGxmKEQe62\nqedqTLY1a9Zk5Y0bN2Zli0yq1F7ps8ZcUxprtFu107oMUPuz7hwzepyycasd3JBaguj4tJ8W6EK1\n6KrhP++887KyLjGM8v/4xz/OjqlL7nve856srEsBe2ZV7eQxul6lXIbmx1CHS+qh6vHWB1wMLCAI\nOOT50GYBt+K50BwOoBaPt1pRxRmmVXPi+dAcjgiGm5CXpet9BFe6/YS1wbcomQ8tFg65iIqlXBKN\n5iq91B1UGvhf3TaNxiqdVY2z1hej/3pMabnGddN+GgU/88zcdKk71lavzj0U1d3VlgKpnWcaU87c\nWfVeqf11+/btWVnjxBlN12XHkiVLsrK2rUsMO1/bu/nmm7OyLpXe/e53Z2VbuqSe9UC13WV+OwO5\nrg7BO1R3oS0BngFOJFD0NS3f91HeiO9wdDWG25q8rJDbJurtBGf4xZTMh2az1ogRIzjxxBOb3D8d\njuGIlStXsnLlygFffygK+VEEe93LwFiCn+xfEXbGXElBPjTNBgLBP7voJij1Uzpu2txYji5IhxM2\n/26lvnpdUWYOdUJRqBOJwrTkMW04wK9+9ausrBps2+0WC70MzZp4qzsVL0618nrcrluwYEF2TCm6\n1qd++jH6rOfec889WVmXTZdeeikAp59+enRMKcQob5kdh0VOUEX0v6+vj/nz5zN//vzs2He/+93C\n/qbaGw4oI+QnE2ZvO/+fgF8QPHM8H5rD0YJDUcg3AvMjx0vlQ7NwSUX7ePVYKnC+HS9zrtrUNaOq\noYybpdWhO71SSQL0HJupNMfYvHnzon2+4447svK9994LpEM3aeglU8LZPm+A2bNnZ2Xd9abKPVPC\nKTtRVqO+1Do7x+6XHtN7YemqIWcqc+fOzY7Nn5//nJTp6fhi9vWB2snL5EKL7XAcqLAeikLucDgq\n4FDVrjscjpLouZncbNuxwAxVFCKpc5QmKz2OJbdXxVwqzE/seCpqp9anb2/bkab9URu30mel8b/+\n9a+BZgqfspPPmjULgAsvvDA7NmPGjKysND8Wsknptbqn/uIXv8jK6gdQtLxJhVjasWMH0KxsvP/+\n+7OyKv2U0ptiUMNq6f1M/V5iO/HK0PXYMafrDocjChdyh6PL0XNCHnNrjQV/KLoe4pQxVYdqsGP5\nv2K5yyAedTRF4TQlsLrRxvqplDmVoMBcbXWXmvZz0aJFWfmKK64Amm3nRx55ZL++t5YNSn217w88\n8EC0z7EkCTpOfb56jj0HrUv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d456df10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5afbeff10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8KryihBBWd7R1idxF6xR2+pxSdwuQEOVmi/Kpefny9N46hWtE0aOtoCb8WLVq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\nkUgkEolEIpGY6vj/TJypH2DLFzkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d4145550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ntrain_loaded = trainimg_loaded.shape[0]\n",
    "batch_size = 10;\n",
    "randidx = np.random.randint(ntrain_loaded, size=batch_size)\n",
    "for i in randidx: \n",
    "    currimg = np.reshape(trainimg_loaded[i, :], (imgsize[0], -1))\n",
    "    currlabel_onehot = trainlabel_loaded[i, :]\n",
    "    currlabel = np.argmax(currlabel_onehot) \n",
    "    if use_gray:\n",
    "        currimg = np.reshape(trainimg[i, :], (imgsize[0], -1))\n",
    "        plt.matshow(currimg, cmap=plt.get_cmap('gray'))\n",
    "        plt.colorbar()\n",
    "    else:\n",
    "        currimg = np.reshape(trainimg[i, :], (imgsize[0], imgsize[1], 3))\n",
    "        plt.imshow(currimg)\n",
    "    title_string = \"[%d] %d-class\" % (i, currlabel)\n",
    "    plt.title(title_string) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOT RANDOMLY SELECTED TEST IMAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SlpwBOmm19l/3z41iq3JLx0WVW6aE0zTBy5cvr8qRgsn6qRRe71kVfZokwZZd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5ae1c9e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IzV9jancq/pwS202WCkyRd90B2oWmIZmhHpL5mcT5nyB4pSbxkdxxKqbNkTwWknlW7ETg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\nHxEL+fO9yAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d45528d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4G6XuAIqXzTDT5yYS0xKjFsixiZB/lnIiRvHHFCE/Dvi/rf8TiQSjJ+RN6PoPKd43ijdQ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\n4/8DwlGaAcR1/LIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5afbefd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do batch stuff using loaded data \n",
    "ntest_loaded = testimg_loaded.shape[0]\n",
    "batch_size   = 3;\n",
    "randidx      = np.random.randint(ntest_loaded, size=batch_size)\n",
    "for i in randidx: \n",
    "    currimg = np.reshape(testimg_loaded[i, :], (imgsize[0], -1))\n",
    "    currlabel_onehot = testlabel_loaded[i, :]\n",
    "    currlabel = np.argmax(currlabel_onehot) \n",
    "    \n",
    "    if use_gray:\n",
    "        currimg = np.reshape(testimg[i, :], (imgsize[0], -1))\n",
    "        plt.matshow(currimg, cmap=plt.get_cmap('gray'))\n",
    "        plt.colorbar()\n",
    "    else:\n",
    "        currimg = np.reshape(testimg[i, :], (imgsize[0], imgsize[1], 3))\n",
    "        plt.imshow(currimg)\n",
    "    title_string = \"[%d] %d-class\" % (i, currlabel)\n",
    "    plt.title(title_string) \n",
    "    plt.show() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
